ArticleFrontiers in digital health2026
Construction of patient trajectories to model clinical trial outcomes: application to myasthenia gravis.
Article in Frontiers in digital health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Bridging the Heterogeneity of Myasthenia Gravis Scores as a Foundational Step Towards the Construction of a Digital Twin.Biomedicines · 2025Article
- Bridging the Heterogeneity of Myasthenia Gravis Severity Scores for Digital Twin Development.medRxiv : the preprint server for health sciences · 2025Article
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Introduction: Accurate prediction of patient outcomes in clinical trials is crucial for the timely assessment of treatment efficacy. This study proposes a novel approach to predict patient response using longitudinal clinical data. Methods: We construct temporal trajectories from longitudinal data and extrapolate these trajectories to forecast individual patient outcomes. Additionally, we assess when new patients align with established response patterns. The approach is evaluated using data from the MGTX trial involving patients with myasthenia gravis. Results: Our analysis demonstrates the predictability of patient trajectories and enables automatic clustering of patients based on treatment success. The clustering reveals potential associations with age and smoking status. Discussion: These findings highlight the potential of trajectory-based methods for early prediction of treatment response in clinical trials. We also discuss possible confounding factors that may influence the observed associations and predictive performance.
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Registered trials
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